Classifying algorithms for SIFT-MS technology and medical diagnosis

Moorhead, Katherine T.; Lee, D.; Chase, J. Geoffrey; Moot, Andrew R.; Ledingham, Katherine M.; Scotter, Jennifer M.; Allardyce, Randall A.; Senthilmohan, Senti T.; Endre, Zoltán H.

doi:10.1016/j.cmpb.2007.11.011

Cited by 26 publications

(21 citation statements)

References 4 publications

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“…Abnormal concentrations of the breath VOCs are reported to correlate with unhealthy/injurious body/organ conditions; for instance, acetone gas for diabetes [2], trimethylamine for uremic patients [3] and ammonia gas for renal disease [4]. Hence, VOCs can potentially be used as disease-specific biomarkers for non-invasive early detection or monitoring from breath.…”

Section: Introductionmentioning

confidence: 99%

A Sub-ppm Acetone Gas Sensor for Diabetes Detection Using 10 nm Thick Ultrathin InN FETs

Kao

Hsu

Chang

et al. 2012

Sensors

137

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An indium nitride (InN) gas sensor of 10 nm in thickness has achieved detection limit of 0.4 ppm acetone. The sensor has a size of 1 mm by 2.5 mm, while its sensing area is 0.25 mm by 2 mm. Detection of such a low acetone concentration in exhaled breath could enable early diagnosis of diabetes for portable physiological applications. The ultrathin InN epilayer extensively enhances sensing sensitivity due to its strong electron accumulation on roughly 5–10 nm deep layers from the surface. Platinum as catalyst can increase output current signals by 2.5-fold (94 vs. 37.5 μA) as well as reduce response time by 8.4-fold (150 vs. 1,260 s) in comparison with bare InN. More, the effect of 3% oxygen consumption due to breath inhalation and exhalation on 2.4 ppm acetone gas detection was investigated, indicating that such an acetone concentration can be analyzed in air.

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Section: Introductionmentioning

confidence: 99%

A Sub-ppm Acetone Gas Sensor for Diabetes Detection Using 10 nm Thick Ultrathin InN FETs

Kao

Hsu

Chang

et al. 2012

Sensors

137

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“…Furthermore, adaptations to the SPME sample collection system have improved VOC levels collected from breath and reduced background signals from the local environment . More recently, scientists have explored the use of more sensitive detection methods and robust bioinformatics software that included selected ion flow tube-mass spectrometry (SIFT-MS), time of flight mass spectrometry (TOF-MS), and proton transfer reaction mass spectrometry (PTR-MS; Moorhead et al, 2008;Buszewski et al, 2007). Beyond the application of VOC analysis in biomedical biomarker research, more recently studies have shown emissions of VOCs from microbes (Crespo et al, 2008) which can act as unique signatures that can be indicative of a specific species.…”

Section: B Voc Biomarker Analysismentioning

confidence: 99%

Targeted metabolomics and mass spectrometry

Dudley

Yousef

Wang

et al. 2010

Advances in Protein Chemistry and Structural Biology

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“…Kernel density estimates can be used for classification and identification of potential diagnostic biomarkers (Moorhead et al 2008). Clinically validated sepsis scores at each patient hour are used to develop probability density profiles for each of the two data sets: Groups j and k, for each clinical predictor.…”

Section: Classificationmentioning

confidence: 99%

A fast and accurate diagnostic test for severe sepsis using kernel classifiers

Parente

Lee

Lin

et al. 2010

UKACC International Conference on CONTROL 2010

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Abstract:Severe sepsis occurs frequently in the intensive care unit (ICU) and is a leading cause of admission, mortality, and cost. Treatment guidelines recommend early intervention, however gold standard blood culture test results may return in up to 48 hours. Insulin sensitivity (SI) is known to decrease with worsening condition and inflammatory response, and could thus be used to aid clinical treatment decisions. Some glycemic control protocols are able to accurately identify SI in real-time.A biomarker for severe sepsis was developed from retrospective SI and concurrent temperature, heart rate, respiratory rate, blood pressure, and SIRS score from 36 adult patients with sepsis. Patients were identified as having sepsis based on a clinically validated sepsis score (ss) of 2 or higher (ss = 0-4 for increasing severity). Kernel density estimates were used for the development of joint probability density profiles for ss ≥ 2 and ss < 2 data hours (213 and 5858 respectively of 6071 total hours) and for classification. From the receiver operator characteristic (ROC) curve, the optimal probability cutoff values for classification were determined for in-sample and out-of-sample estimates.A biomarker including concurrent insulin sensitivity and clinical data for the diagnosis of severe sepsis (ss ≥ 2) achieves 69-94% sensitivity, 75-94% specificity, 0.78-0.99 AUC, 3-17 LHR+, 0.06-0.4 LHR-, 9-38% PPV, 99-100% NPV, and a diagnostic odds ratio of 7-260 for optimal probability cutoff values of 0.32 and 0.27 for in-sample and out-of-sample data, respectively. The overall result lies between these minimum and maximum error bounds. Thus, the clinical biomarker shows good to high accuracy and may provide useful information as a real-time diagnostic test for severe sepsis.

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Classifying algorithms for SIFT-MS technology and medical diagnosis

Cited by 26 publications

References 4 publications

A Sub-ppm Acetone Gas Sensor for Diabetes Detection Using 10 nm Thick Ultrathin InN FETs

A Sub-ppm Acetone Gas Sensor for Diabetes Detection Using 10 nm Thick Ultrathin InN FETs

Targeted metabolomics and mass spectrometry

A fast and accurate diagnostic test for severe sepsis using kernel classifiers

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